{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:42:00Z","timestamp":1775068920161,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031261176","type":"print"},{"value":"9783031261183","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-26118-3_13","type":"book-chapter","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T10:03:25Z","timestamp":1675159405000},"page":"169-177","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MixKd: Mix Data Augmentation Guided Knowledge Distillation for\u00a0Plant Leaf Disease Recognition"],"prefix":"10.1007","author":[{"given":"Haotian","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Meili","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,1]]},"reference":[{"key":"13_CR1","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/978-981-15-2612-1_21","volume-title":"International Conference on Communication, Computing and Electronics Systems","author":"J Arunnehru","year":"2020","unstructured":"Arunnehru, J., Vidhyasagar, B.S., Anwar Basha, H.: Plant leaf diseases recognition using convolutional neural network and\u00a0transfer learning. In: Bindhu, V., Chen, J., Tavares, J.M.R.S. (eds.) International Conference on Communication, Computing and Electronics Systems. LNEE, vol. 637, pp. 221\u2013229. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-2612-1_21"},{"issue":"3","key":"13_CR2","first-page":"220","volume":"22","author":"A Bhat","year":"2020","unstructured":"Bhat, A., Wani, M.H., Bhat, G.M., Qadir, A., Qureshi, I., Ganaie, S.A.: Health cost and economic loss due to excessive pesticide use in apple growing region of Jammu and Kashmir. J. Appl. Hortic. 22(3), 220\u2013225 (2020)","journal-title":"J. Appl. Hortic."},{"issue":"1","key":"13_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","volume":"72","author":"S Sankaran","year":"2010","unstructured":"Sankaran, S., Mishra, A., Ehsani, R., Davis, C.: A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72(1), 1\u201313 (2010)","journal-title":"Comput. Electron. Agric."},{"issue":"12","key":"13_CR4","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.1094\/PDIS-03-14-0290-RE","volume":"98","author":"JGA Barbedo","year":"2014","unstructured":"Barbedo, J.G.A.: An automatic method to detect and measure leaf disease symptoms using digital image processing. Plant Dis. 98(12), 1709\u20131716 (2014)","journal-title":"Plant Dis."},{"issue":"2","key":"13_CR5","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/s10658-016-1007-6","volume":"147","author":"JGA Barbedo","year":"2017","unstructured":"Barbedo, J.G.A.: A new automatic method for disease symptom segmentation in digital photographs of plant leaves. Eur. J. Plant Pathol. 147(2), 349\u2013364 (2017). https:\/\/doi.org\/10.1007\/s10658-016-1007-6","journal-title":"Eur. J. Plant Pathol."},{"issue":"7","key":"13_CR6","doi-asserted-by":"publisher","first-page":"617","DOI":"10.3390\/agriculture11070617","volume":"11","author":"P Bansal","year":"2021","unstructured":"Bansal, P., Kumar, R., Kumar, S.: Disease detection in apple leaves using deep convolutional neural network. Agriculture 11(7), 617 (2021)","journal-title":"Agriculture"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Khan, A.I., Quadri, S.M.K., Banday, S.: Deep learning for apple diseases: classification and identification. arXiv preprint arXiv:2007.02980 (2020)","DOI":"10.1504\/IJCISTUDIES.2021.113831"},{"issue":"1","key":"13_CR8","first-page":"211","volume":"15","author":"S Arivazhagan","year":"2013","unstructured":"Arivazhagan, S., Shebiah, R.N., Ananthi, S., Varthini, S.V.: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int. CIGR J. 15(1), 211\u2013217 (2013)","journal-title":"Agric. Eng. Int. CIGR J."},{"key":"13_CR9","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"13_CR11","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"13_CR12","unstructured":"Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, vol. 2, no. 7 (2015)"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3967\u20133976 (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Yuan, L., Tay, F.E.H., Li, G., Wang, T., Feng, J.: Revisiting knowledge distillation via label smoothing regularization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3903\u20133911 (2020)","DOI":"10.1109\/CVPR42600.2020.00396"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., Batra, N.: PlantDoc: a dataset for visual plant disease detection. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, pp. 249\u2013253 (2020)","DOI":"10.1145\/3371158.3371196"}],"container-title":["Lecture Notes in Computer Science","Green, Pervasive, and Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26118-3_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T10:06:24Z","timestamp":1675159584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26118-3_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031261176","9783031261183"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26118-3_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GPC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Green, Pervasive, and Cloud Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chengdu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"gpc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2022.gpc-conf.org\/home.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"104","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}